Komparasi Algoritma K-Mean dan Hierarchical untuk Pengelompokan Pengaruh Covid-19 Terhadap Pendidikan - Mayadi; Setiawati, Siti (Journal of Informatic and Information Security, 2021)
The purpose of this study is to classify the impact of covid-19 on the world of education. Covid-19 is a virus that has had a major impact on politics, economy, culture, sports, education and other fields. The impact in the field of education is the closure of schools, universities, and institutions so that learning activities are carried out online from home. The method in this study begins by taking a dataset sourced from the public dataset https://www.kaggle.com/ to obtain data on the impact of COVID-19 on education. The next stage is preprocessing the data to filter the attributes that have the most influence on education using excel and python programming, the dataset has been continued to create patterns using machine learning algorithms, namely hierarchical clustering and k-mean clustering the clustering algorithm used. Clustering is the process of grouping similar objects into different groups or dividing a data set into subsets based on distance measurements. The expected result of this research is the comparison of the k-mean and hierarchical clustering algorithms which will have the highest accuracy in classifying the impact of covid-19 on education.